Administering Watson Machine Learning

Scaling Watson Machine Learning based on workload

IBM Cloud Pak for Data supports scaling of add-on services. If you are a cluster administrator, you can scale Watson Machine Learning after installation from small (the default configuration) to medium to manage a larger workload.

Scaling changes the capacity of services by adjusting the number of pods that are available. Pods act as servers with a set resource limitation that run an application or function. Pods consume resources such as core and memory when components distribute tasks to them. Scaling the pods to medium, for example, increases the processing capacity of the application.

For details, refer to Scaling services

Configure Watson Machine Learning Accelerator for Deep Learning Experiments

Deep Learning Experiments accelerate the iterative process of training a deep learning model by simplifying the process to train models in parallel with an on-demand GPU compute cluster. To build, train, and deploy deep learning experiments, you must configure Watson Machine Learning Accelerator to work with Watson Machine Learning. For details, refer to Connecting Watson Machine Learning Accelerator to Watson Machine Learning.

Parent topic: IBM Watson Machine Learning